The existing wireless mobile communication systems provide several types of services and mostly depend on channel coding to overcome any inferiority of channels. However, due to the increasing demands, for example for a high-quality multimedia services, in which users can communicate with anyone regardless of time and place, the existing services have evolved data-oriented services. Accordingly, there is a high demand for next generation wireless transmission technology for transmitting the larger amount of data at a lower error rate. In particular, it is very important to transmit data at a high rate in a link in which the amount of required data is large.
For the next generation wireless communication, various antenna systems have been proposed. For example, a MIMO system, i.e., a typical antenna system, increases spectrum efficiency through all of transmission antennas without excessive use of a frequency bandwidth. Generally, MIMO is classified into Space-Time Coding (STC), Diversity, Beam Forming (BF), and Spatial Multiplexing (SM) according to the transmission structure and scheme of a transmitter, all of which provide high data rate and reliability.
A MIMO system adopts multiple antennas or array antenna to transmit/receive data in the transmitter and receiver. Multiple antennas are provided in different spatial positions, with different fading features, thus the received signals of adjacent antennas can be approximated as uncorrelated entirely as long as the spacing between adjacent antennas for transmitting/receiving signals in the MIMO system is large enough. The MIMO system takes full advantage of the spatial characteristics of multipath for implementing space diversity transmission and reception.
FIG. 1 illustrates an exemplary and simplified MIMO system 100 constructed by M Tx antennas 103 and N Rx antennas 104. As mentioned earlier, the antenna spacing between the Tx antennas and Rx antennas in the MIMO system in FIG. 1 is generally big enough, to guarantee the spatial un-correlation of signals. As FIG. 1 shows, in the transmitter, MIMO architecture unit 101 first transforms a channel of data stream into M channels of parallel sub data streams; then, multiple access transform unit 102 performs multiplex processing; finally, the corresponding M Tx antennas 103 transmit the signal simultaneously into the wireless channels. The MIMO architecture unit 101 can adopt any one of the MIMO processing methods, such as STTC (Space Time Trellis Code), space-time block code, space-time Turbo code, BLAST code and etc. While multiple access transform unit 102 can implements TDD, FDD or CDMA. Efficient demodulation of MIMO is non-trivial and currently a hot research topic.
Existing maximum-likelihood detection methods for MIMO are in general highly computationally complex. Much effort has therefore been focused on finding reduced-complexity MIMO demodulation techniques that still give close to optimal performance.
One example of a low-complexity method is Interference Rejection Combing (IRC), which gives good performance at low SNR, but falls well below an optimal demodulator at higher SNRs, closer to the saturation region of the modulation scheme. Other techniques, like sphere decoding (SD), are exact and very fast at high SNR, but extremely complex at low SNRs, at least if configured to give reasonably good performance, since the complexity is then exponential in the problem size, see for example Joakim Jaldén, “On the Complexity of Sphere Decoding in Digital Communications”, IEEE Transactions on Signal Processing, April 2005. Also, SD in its basic form generates only hard bits, and soft value generation is complex or relies on ad hoc methods.
Another method, so-called m-algorithm has complexity that is not very dependent on SNR, and shows performance close to an optimal demodulator with substantially lower complexity. However, fairly many retained paths are needed for reaching this performance, making its complexity still rather high. Like the SD, the m-algorithm also relies on ad hoc methods for soft value generation. An approach towards low-complexity exact ML has been proposed in Toshiaki Koike, Daisuke Nishikawa, and Susumu Yoshida, “Metric-segmented Low-complexity ML Detection for Spectrum-efficient Multiple-antenna Systems”, VTC 2005 Fall, but also this method only provides hard bits.
EP 1422896 relates to a method of decoding a communications signal in a digital communications system. The communications signal is modulated according to a modulation scheme, such as 16-QAM, including amplitude information. The method comprises the steps of generating a likelihood value for a received communications signal; decoding the communications signal based on at least the generated likelihood value. The method further comprises providing a reliability indication of the amplitude information conveyed by the received communications signal; and generating the likelihood value on the basis of the provided reliability indication of the amplitude information.